{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#HW1： 线性回归\n",
    "#任务：    在 Capital Bikeshare （美国 Washington, D.C.的一个共享单车公司）提供的自行\n",
    "#      车数据上进行回归分析。训练数据为 2011 年的数据，要求预测 2012 年每天的单车\n",
    "#      共享数量。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011/1/1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.344167</td>\n",
       "      <td>0.363625</td>\n",
       "      <td>0.805833</td>\n",
       "      <td>0.160446</td>\n",
       "      <td>331</td>\n",
       "      <td>654</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011/1/2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "      <td>131</td>\n",
       "      <td>670</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011/1/3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>0.248309</td>\n",
       "      <td>120</td>\n",
       "      <td>1229</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011/1/4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "      <td>108</td>\n",
       "      <td>1454</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011/1/5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>82</td>\n",
       "      <td>1518</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant    dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "0        1  2011/1/1       1   0     1        0        6           0   \n",
       "1        2  2011/1/2       1   0     1        0        0           0   \n",
       "2        3  2011/1/3       1   0     1        0        1           1   \n",
       "3        4  2011/1/4       1   0     1        0        2           1   \n",
       "4        5  2011/1/5       1   0     1        0        3           1   \n",
       "\n",
       "   weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
       "0           2  0.344167  0.363625  0.805833   0.160446     331         654   \n",
       "1           2  0.363478  0.353739  0.696087   0.248539     131         670   \n",
       "2           1  0.196364  0.189405  0.437273   0.248309     120        1229   \n",
       "3           1  0.200000  0.212122  0.590435   0.160296     108        1454   \n",
       "4           1  0.226957  0.229270  0.436957   0.186900      82        1518   \n",
       "\n",
       "    cnt  \n",
       "0   985  \n",
       "1   801  \n",
       "2  1349  \n",
       "3  1562  \n",
       "4  1600  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#导入所需工具包\n",
    "import numpy as np \n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "\n",
    "#数据读取\n",
    "BikeSharing_data = pd.read_csv(\"day.csv\")\n",
    "\n",
    "BikeSharing_data.head()#了解 列 概况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 731 entries, 0 to 730\n",
      "Data columns (total 16 columns):\n",
      "instant       731 non-null int64\n",
      "dteday        731 non-null object\n",
      "season        731 non-null int64\n",
      "yr            731 non-null int64\n",
      "mnth          731 non-null int64\n",
      "holiday       731 non-null int64\n",
      "weekday       731 non-null int64\n",
      "workingday    731 non-null int64\n",
      "weathersit    731 non-null int64\n",
      "temp          731 non-null float64\n",
      "atemp         731 non-null float64\n",
      "hum           731 non-null float64\n",
      "windspeed     731 non-null float64\n",
      "casual        731 non-null int64\n",
      "registered    731 non-null int64\n",
      "cnt           731 non-null int64\n",
      "dtypes: float64(4), int64(11), object(1)\n",
      "memory usage: 91.5+ KB\n"
     ]
    }
   ],
   "source": [
    "BikeSharing_data.info()#数据基本信息\n",
    "#BikeSharing_data.describe()#属性的统计特性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23f58333320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "weathersit and temp = 0.99\n",
      "casual and registered = 0.95\n",
      "instant and season = 0.87\n",
      "dteday and yr = 0.83\n",
      "windspeed and registered = 0.67\n",
      "instant and casual = 0.66\n",
      "temp and registered = 0.63\n",
      "instant and registered = 0.63\n",
      "weathersit and registered = 0.63\n",
      "season and casual = 0.59\n",
      "workingday and atemp = 0.59\n",
      "season and registered = 0.57\n",
      "temp and casual = 0.54\n",
      "temp and windspeed = 0.54\n",
      "weathersit and windspeed = 0.54\n",
      "weathersit and casual = 0.54\n",
      "weekday and windspeed = 0.52\n"
     ]
    }
   ],
   "source": [
    "#数据探索\n",
    "#分析两两之间的相关性，以便降维\n",
    "BS_cols = BikeSharing_data.columns\n",
    "BS_cols_corr = BikeSharing_data.corr().abs()\n",
    "\n",
    "plt.subplots(figsize = (15,10))\n",
    "sns.heatmap(BS_cols_corr,annot = True)\n",
    "\n",
    "sns.heatmap(BS_cols_corr,mask = BS_cols_corr < 1,cbar = False)#为了将1的和0.99的分开\n",
    "plt.show()\n",
    "\n",
    "threshold = 0.5\n",
    "corr_list = []\n",
    "size = BS_cols_corr.shape[0]\n",
    "for i in range(0, size):\n",
    "    for j in range(i+1,size): \n",
    "        if (BS_cols_corr.iloc[i,j] >= threshold and BS_cols_corr.iloc[i,j] < 1) :#因为上方相关系数（采用abs(）都为正的\n",
    "            corr_list.append([BS_cols_corr.iloc[i,j],i,j])\n",
    "s_corr_list = sorted(corr_list,key=lambda x: -abs(x[0]))\n",
    "for v,i,j in s_corr_list:\n",
    "    print (\"%s and %s = %.2f\" % (BS_cols[i],BS_cols[j],v))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#丢弃一些不必要的特征\n",
    "BikeSharing_data = BikeSharing_data.drop(\"dteday\",axis = 1)\n",
    "\n",
    "#训练数据和测试数据分割（将 2012 年的数据作为测试数据）及数据准备\n",
    "\n",
    "#因为在作业中有说明：蓝色标记的后三个特征均为要预测的 y，作业里只需对 cnt 进行预测，\n",
    "#黑色标记的特征为输入特征 x，所以做下列操作：输入为去掉\"casual\",\"registered\",\"cnt\"这3列，\n",
    "#预测只预测\"cnt\"\n",
    "\n",
    "cols = [\"casual\",\"registered\",\"cnt\"]\n",
    "\n",
    "#训练数据\n",
    "BikeSharing_train_2011 = BikeSharing_data[:333]\n",
    "x_2011_train = BikeSharing_train_2011.drop(cols,axis = 1)\n",
    "y_2011_train = BikeSharing_train_2011[\"cnt\"].values\n",
    "#测试数据\n",
    "BikeSharing_test_2012 = BikeSharing_data[334:]\n",
    "x_2012_test = BikeSharing_test_2012.drop(cols,axis = 1)\n",
    "y_2012_test = BikeSharing_test_2012[\"cnt\"].values\n",
    "\n",
    "columns = x_2011_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Dell\\Documents\\anaconda\\lib\\site-packages\\sklearn\\utils\\validation.py:475: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n"
     ]
    }
   ],
   "source": [
    "#数据预处理/特征工程\n",
    "\n",
    "#数据标准化\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "ss_x = StandardScaler()\n",
    "ss_y = StandardScaler()\n",
    "\n",
    "x_2011_train = ss_x.fit_transform(x_2011_train)\n",
    "x_2012_test  = ss_x.transform(x_2012_test)\n",
    "\n",
    "y_2011_train = ss_x.fit_transform(y_2011_train.reshape(-1,1))\n",
    "y_2012_test  = ss_x.transform(y_2012_test.reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#数据归一化\n",
    "#Season,yr,mnth,holiday,weekday,workingday,weathersit为类别型特征\n",
    "#temp,atemp,hum，windspeed，cnt为数值型特征,对其做归一化处理\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "x_2011_train = scaler.fit_transform(x_2011_train)\n",
    "x_2012_test  = scaler.fit_transform(x_2012_test)\n",
    "y_2011_train = scaler.fit_transform(y_2011_train)\n",
    "y_2012_test  = scaler.fit_transform(y_2012_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of RidgeCV on test is 0.48527801419878225\n",
      "The r2 score of RidgeCV on train is 0.7625632827206594\n"
     ]
    }
   ],
   "source": [
    "#模型确定和模型训练\n",
    "\n",
    "#岭回归／L2正则\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "# 使用默认配置初始化\n",
    "lr = LinearRegression()\n",
    "\n",
    "lr.fit(x_2011_train, y_2011_train)\n",
    "\n",
    "# y_test_pred_lr = lr.predict(X_test)\n",
    "# y_train_pred_lr = lr.predict(X_train)\n",
    "\n",
    "fs = pd.DataFrame({\"columns\":list(columns), \"coef\":list((lr.coef_.T))})\n",
    "fs.sort_values(by=['coef'],ascending=False)\n",
    "\n",
    "\n",
    "from sklearn.linear_model import  RidgeCV\n",
    "\n",
    "alphas = [ 0.01, 0.1, 1, 10,100]\n",
    "#三部曲\n",
    "ridge = RidgeCV(alphas=alphas, store_cv_values=True) \n",
    "\n",
    "ridge.fit(x_2011_train, y_2011_train) \n",
    "\n",
    "y_2012_test_pred_ridge = ridge.predict(x_2012_test)\n",
    "y_2011_train_pred_ridge = ridge.predict(x_2011_train)\n",
    "#模型评估\n",
    "print( 'The r2 score of RidgeCV on test is', r2_score(y_2012_test, y_2012_test_pred_ridge))\n",
    "print( 'The r2 score of RidgeCV on train is', r2_score(y_2011_train, y_2011_train_pred_ridge))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23f584e7f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alpha is: 1.0\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coef_lr</th>\n",
       "      <th>coef_ridge</th>\n",
       "      <th>columns</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>[0.746646484247655]</td>\n",
       "      <td>[0.352263401970961]</td>\n",
       "      <td>temp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[0.4283053838942018]</td>\n",
       "      <td>[0.12314428860153148]</td>\n",
       "      <td>mnth</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[0.07422879712209646]</td>\n",
       "      <td>[0.06326214354464399]</td>\n",
       "      <td>season</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>[0.020459746849911663]</td>\n",
       "      <td>[0.020128081288289357]</td>\n",
       "      <td>weekday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[-5.551115123125783e-16]</td>\n",
       "      <td>[0.0]</td>\n",
       "      <td>yr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>[-0.00547381540193098]</td>\n",
       "      <td>[-0.004235982930751725]</td>\n",
       "      <td>workingday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[-0.051205272063546775]</td>\n",
       "      <td>[-0.0419574589295823]</td>\n",
       "      <td>holiday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>[-0.07261180477843276]</td>\n",
       "      <td>[-0.06979844535262088]</td>\n",
       "      <td>hum</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>[-0.12078572049471392]</td>\n",
       "      <td>[0.2864912011034445]</td>\n",
       "      <td>atemp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>[-0.17838087838671413]</td>\n",
       "      <td>[-0.1525341146785696]</td>\n",
       "      <td>windspeed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>[-0.2019691048350778]</td>\n",
       "      <td>[-0.1965253340740967]</td>\n",
       "      <td>weathersit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[-0.30976010104218143]</td>\n",
       "      <td>[0.03978890716226116]</td>\n",
       "      <td>instant</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     coef_lr               coef_ridge     columns\n",
       "8        [0.746646484247655]      [0.352263401970961]        temp\n",
       "3       [0.4283053838942018]    [0.12314428860153148]        mnth\n",
       "1      [0.07422879712209646]    [0.06326214354464399]      season\n",
       "5     [0.020459746849911663]   [0.020128081288289357]     weekday\n",
       "2   [-5.551115123125783e-16]                    [0.0]          yr\n",
       "6     [-0.00547381540193098]  [-0.004235982930751725]  workingday\n",
       "4    [-0.051205272063546775]    [-0.0419574589295823]     holiday\n",
       "10    [-0.07261180477843276]   [-0.06979844535262088]         hum\n",
       "9     [-0.12078572049471392]     [0.2864912011034445]       atemp\n",
       "11    [-0.17838087838671413]    [-0.1525341146785696]   windspeed\n",
       "7      [-0.2019691048350778]    [-0.1965253340740967]  weathersit\n",
       "0     [-0.30976010104218143]    [0.03978890716226116]     instant"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#可视化\n",
    "mse_mean = np.mean(ridge.cv_values_, axis = 0)\n",
    "plt.plot(np.log10(alphas), mse_mean.reshape(len(alphas),1)) \n",
    "plt.xlabel('log(alpha)')\n",
    "plt.ylabel('mse')\n",
    "plt.show()\n",
    "\n",
    "print ('alpha is:', ridge.alpha_)\n",
    "\n",
    "fs = pd.DataFrame({\"columns\":list(columns), \"coef_lr\":list((lr.coef_.T)), \"coef_ridge\":list((ridge.coef_.T))})\n",
    "fs.sort_values(by=['coef_lr'],ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Dell\\Documents\\anaconda\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:1094: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of LassoCV on test is 0.37773893483074794\n",
      "The r2 score of LassoCV on train is 0.7470540568522053\n"
     ]
    }
   ],
   "source": [
    "#Lasso\n",
    "from sklearn.linear_model import LassoCV\n",
    "\n",
    "lasso = LassoCV()\n",
    "\n",
    "lasso.fit(x_2011_train, y_2011_train) \n",
    "\n",
    "y_2012_test_pred_lasso = lasso.predict(x_2012_test)\n",
    "y_2011_train_pred_lasso = lasso.predict(x_2011_train)\n",
    "#模型评估\n",
    "print ('The r2 score of LassoCV on test is', r2_score(y_2012_test, y_2012_test_pred_lasso))\n",
    "print ('The r2 score of LassoCV on train is', r2_score(y_2011_train, y_2011_train_pred_lasso))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23f584fe908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alpha is: 0.0038423396552810674\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coef_lasso</th>\n",
       "      <th>coef_lr</th>\n",
       "      <th>coef_ridge</th>\n",
       "      <th>columns</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.586511</td>\n",
       "      <td>[0.746646484247655]</td>\n",
       "      <td>[0.352263401970961]</td>\n",
       "      <td>temp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.125748</td>\n",
       "      <td>[0.4283053838942018]</td>\n",
       "      <td>[0.12314428860153148]</td>\n",
       "      <td>mnth</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.079584</td>\n",
       "      <td>[0.07422879712209646]</td>\n",
       "      <td>[0.06326214354464399]</td>\n",
       "      <td>season</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[0.020459746849911663]</td>\n",
       "      <td>[0.020128081288289357]</td>\n",
       "      <td>weekday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[-5.551115123125783e-16]</td>\n",
       "      <td>[0.0]</td>\n",
       "      <td>yr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>[-0.00547381540193098]</td>\n",
       "      <td>[-0.004235982930751725]</td>\n",
       "      <td>workingday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>[-0.051205272063546775]</td>\n",
       "      <td>[-0.0419574589295823]</td>\n",
       "      <td>holiday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>[-0.07261180477843276]</td>\n",
       "      <td>[-0.06979844535262088]</td>\n",
       "      <td>hum</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[-0.12078572049471392]</td>\n",
       "      <td>[0.2864912011034445]</td>\n",
       "      <td>atemp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-0.021270</td>\n",
       "      <td>[-0.17838087838671413]</td>\n",
       "      <td>[-0.1525341146785696]</td>\n",
       "      <td>windspeed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.189987</td>\n",
       "      <td>[-0.2019691048350778]</td>\n",
       "      <td>[-0.1965253340740967]</td>\n",
       "      <td>weathersit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[-0.30976010104218143]</td>\n",
       "      <td>[0.03978890716226116]</td>\n",
       "      <td>instant</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    coef_lasso                   coef_lr               coef_ridge     columns\n",
       "8     0.586511       [0.746646484247655]      [0.352263401970961]        temp\n",
       "3     0.125748      [0.4283053838942018]    [0.12314428860153148]        mnth\n",
       "1     0.079584     [0.07422879712209646]    [0.06326214354464399]      season\n",
       "5     0.000000    [0.020459746849911663]   [0.020128081288289357]     weekday\n",
       "2     0.000000  [-5.551115123125783e-16]                    [0.0]          yr\n",
       "6    -0.000000    [-0.00547381540193098]  [-0.004235982930751725]  workingday\n",
       "4    -0.000000   [-0.051205272063546775]    [-0.0419574589295823]     holiday\n",
       "10   -0.000000    [-0.07261180477843276]   [-0.06979844535262088]         hum\n",
       "9     0.000000    [-0.12078572049471392]     [0.2864912011034445]       atemp\n",
       "11   -0.021270    [-0.17838087838671413]    [-0.1525341146785696]   windspeed\n",
       "7    -0.189987     [-0.2019691048350778]    [-0.1965253340740967]  weathersit\n",
       "0     0.000000    [-0.30976010104218143]    [0.03978890716226116]     instant"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mses = np.mean(lasso.mse_path_, axis = 1)\n",
    "plt.plot(np.log10(lasso.alphas_), mses) \n",
    "#plt.plot(np.log10(lasso.alphas_)*np.ones(3), [0.3, 0.4, 1.0])\n",
    "plt.xlabel('log(alpha)')\n",
    "plt.ylabel('mse')\n",
    "plt.show()    \n",
    "\n",
    "print ('alpha is:', lasso.alpha_)\n",
    "\n",
    "fs = pd.DataFrame({\"columns\":list(columns), \"coef_lr\":list((lr.coef_.T)), \"coef_ridge\":list((ridge.coef_.T)), \"coef_lasso\":list((lasso.coef_.T))})\n",
    "fs.sort_values(by=['coef_lr'],ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9trtnu3t2RoaOdYrIsZUfrGb+hj1cdmo/YnUR4nEJ9dDWB0CimfUncFjrVuCZZtYp5F/3IgYARU21MbM4oAdQ6u417v4tdx/n7jOBVGATgLvvCP48ADxP4BCaiEiLvLl6J9W1zsxxDQ+cSHNCDRJz94PAZ4Ffu/sVBMYxjmUpMMLMhphZAnAtMLdBm7nAzcHnVxLY63EzSzo6oG9m04Ead18bPNSVHlweD3waWB1iH0REmvTqJzsYmt6NU/qnRLqUdifUs7bMzM4AbiBwplWz67p7jZndAcwDYoGn3H2NmT0A5Lj7XOBJ4DkzywNKCYQNQG9gnpnVATv45+GrxODy+OA2Qxn0FxE5prw9B/h4ayl3zxhFE8OucgyhBsk3gHuAV4NhMAR4r7mV3P0N4I0Gy75f7/lh4KpG1ttGYFC/4fJKAgPvIiKt5vkl24mPNa7KHhDpUtqlUIPkIFAHXGdmnycwSN5w4FxEpN05XF3Ly7nbuejkPqQnJ0a6nHYp1CD5A3AXgfGIumbaioi0G39duZP9h2u4fvKgSJfSboUaJMXu/uewViIiEgF/WJLP0PRunDFU146cqFCD5H4ze4LA1eZHji5091fDUpWISBtYt3M/ywr28b1LR2uQvQVCDZJbgVEEZv09emjLAQWJiLRbzy8pICEuhs+N1yB7S4QaJKcFryYXEekQ9h2s4pVlhXz61L707JYQ6XLatVCDZLGZjWkwc2+H970/reJwdR3DMpIZ3jvwGJSWpOkTRDqA5xblc7CqllnnDI10Ke1eqEFyFnCzmW0lMEZigLv7qWGrLAqUVlaRs62Ml3ML/7EsIS6GYRnJjMxM5qQ+3RndJ4VRfbvTJ6WLjrGKtBOHq2t5ZuE2zj8pg1F9dCV7S4UaJDPCWkWUeuSGwLWP5Yeq2VxcQd6ewGPj7gMs3VrK68v/OXVYz6R4xvRL4eR+PTilfw/G9u/B4LQkYsKw9+Lu7Nh3iDVF+zlUVUuX+Fi6JsSSmZLIsIxk4mNDvoOySKf0x5zt7K2s4kvnDot0KR1CSEHi7vnhLiSa9egaz/hBPRk/qOe/LC8/WM2G3QdYt3M/63buZ+3O/TyzcBtVNYHzEbp3ieO0AamcNrAHpw1IZfzgnsd9wdPh6lry9lSwYVfwc3btZ03RfvYdrG60fXysMbx3dyYMTuXiU/oyeUgacQoWkX+oqa1j9odbGDcwlclD0iJdTocQ6h6JNKJHUjyThqQxqd5fxuraOjbuPsDqHeWsKCxnZeE+Hnt/CzV1gYkABqUlMX5QKtlZaWRn9WRk7+5U1daxe/9hdpYfZltJJVtKKtkS3AMqKD1IcFUS42IYmdmdGSf34eT+PTi5XwqpXeM5VF3L4epaCssOsW5nIHBeyd3B7xcXkNYtgYtOzuSy0/oxeUgvje9Ip/fG6l1sLz3EfZeM0eHoVmLuHX+mk+zsbM/JidydeQ9X17J6RznLCsrIzS8jN38fJRWBy3ES4mL+sQdzVEJcDFm9khjRuzvDeyczIjOZUX1SyOqVFPLexaGqWt7fWMwbq3by7rrAXd8yUxK57NR+fHb8AMb003Fh6XzcnU//egGHqmt551vnhuXQc0diZrnu3uzdaRUkEeDuFJQeJGdbGet27ic1KZ7MlC706dGFrF7d6JfatVX3HA5V1fLu+t28vryI+Rv2UF3rjO6bwpUTBvC58f1JTdKpj9I5LNmyl2tmL+ZHV4zVlCghUJDUE21BEklllVX8eWURL+cWsrKwnIS4GC45pQ/XTx7MxKye2tWXDu1Lz+Xw8dZSFt07jS7xsZEuJ+qFGiQaI+lkenZL4KYzsrjpjCzW7dzPCx8X8NqyHfxpeRGj+nTn5jOzmDmuH0kJ+qshHUvB3oP8be1uvnreMIVIK9PpPJ3Y6L4pPDDzFJbcN42ffG4sZsa9r65iyo/e5advrWfP/sORLlGk1Ty7aBuxZtw4JSvSpXQ4+m+nkJQQxzUTB3F19kBy8st4asFWHn1/M098uJXPnN6PL587jKEZyZEuU+SEHThczYtLt3PpqX3p06NLpMvpcBQk8g9mxsSsNCZmpbGtpJInF2zlpZzt/DG3kEvH9uVr5w9ndF+d7SXtz8u5hVQcqeHWqUMiXUqHpMF2OaaSiiM8uWArzy3Kp+JIDRef0odvTR/JyMzukS5NJCR1dc75/zef9OREXvnKmZEup10JdbBdYyRyTOnJidw9YxQf3X0BX582gg83lXDRLz/gWy8up2DvwUiXJ9KsDzYVk7/3IDefmRXpUjqssAaJmc0wsw1mlmdm9zTyfqKZvRh8f4mZZQWXJ5jZ02a2ysxWmNl59daZEFyeZ2a/Mp2v2iZ6JMVz5/SRfPjd85l1zlDeXL2Tab+Yzw/+spayyqpIlyfSpN8vzic9OZEZJ/eJdCkdVtiCxMxigYeBi4ExwHVmNqZBs9uAMncfDjwI/CS4/HaA4D1QpgP/Z2ZHa30UmAWMCD465YSSkdKzWwL3Xjya979zPp8bP4CnP9rKOT/7O794e+M/rtYXiRY79h3ivfV7uGbiABLidAAmXML5JzsJyHP3Le5eBcwBZjZoMxN4Nvj8ZWBacA9jDIHb+uLue4B9QLaZ9QVS3H2RBwZ3fgd8Jox9kCZkpnThx587lbe+eQ5nDO3Fr9/bxJk/fo97X13J5uKKSJcnAsALSwoAuG6SrmIPp3CetdUf2F7vdSEwuak27l5jZuVAL2AFMNPM5gADgQnBn3XB7dTfZv+wVC8hGZnZndk3ZbO5uIInF2zlldxC5izdzoVjMvnSucP+bcZkkbZSVVPHnKUFXDCqNwN6JkW6nA4tnEHS2NhFw1PEmmrzFDAayAHygYVATYjbDGzYbBaBQ2AMGqT/jYTbsIxkfnTFWO6cPpLfLdzGs4vymbdmN5OHpPGNaSM4Y1gvTb8ibWreml2UVFTx+SmDI11KhxfOQ1uFBPYijhoAFDXVxszigB5AqbvXuPu33H2cu88EUoFNwfYDmtkmAO4+292z3T07IyOjVTokzUtPTuTOC09i4T0X8F+fHsO2vZVc/8QSrvrtIhZsKol0edKJPLc4n0FpSZwzQv/+wy2cQbIUGGFmQ8wsAbgWmNugzVzg5uDzK4H33N3NLMnMugGY2XSgxt3XuvtO4ICZTQmOpdwEvB7GPsgJ6pYYx21nDeH975zPD2aezI59h/j8k0u4/vHFLCsoi3R50sHl7TnAx1tLuW7SIE0V3wbCdmgrOOZxBzAPiAWecvc1ZvYAkOPuc4EngefMLA8oJRA2AL2BeWZWB+wAbqy36a8AzwBdgTeDD4lSXeJjufGMLK6eOJDnlxTwm/fy+OwjC7lwTCb3XDxKU69IWLy4dDtxMcaVEwY031haTFe2S5uqPFLDUwu28tv3N3Okpo4bJg/i69NG0Os4b0Es0pQjNbWc8b/vMXlIGo9+fkKky2nXdGW7RKVuiXH8x7QRzP/O+VwzcSDPLc7nvJ/N5+G/53GoqjbS5UkH8M7aPZRWVnHNxIHNN5ZWoSCRiMjonsj/XDGWed88h8lDe/GzeRs4/+fz+cOSfAWKtMicpQX0T+3K2RpkbzMKEomoEZndeeLmbF6cNYU+Pbpw32urmfK/7/LDv6wlf29lpMuTdmZ76UEW5JVwVfaAVr1dtRybppGXqDB5aC9e++qZfLy1lN8tzueZhdt48qOtnDcyg5vOzOLcERk6+0aa9cecwDXQV2XrsFZbUpBI1DAzJg/txeShvdi9/zDPLyng+Y8LuPXppQzulcSNUwZz1YSB9EiKj3SpEoVqaut4KaeQc0dm0D+1a6TL6VR0aEuiUmZKF741fSQf3X0Bv7rudNKTE/nhX9cx+X/f4d5XV7Jp94FIlyhR5u21u9m1/7Dm1YoA7ZFIVEuIi+Hy0/px+Wn9WFNUznOL8nl12Q5e+Hg755+Uwe1nD9X0KwLAEwu2MigtiU+Nzox0KZ2O9kik3Ti5Xw9+/LlTWXTvNO6cPpJVO8q5/oklfOaRhfxtzS7q6jr+NVHSuE8KysjNL+PWqVkaZI8ABYm0O2ndEvj6tBEsuPsCfnTFWMoqq5j1XC4XP/Qhf125U4HSCT25YCvdu8RpkD1CFCTSbnWJj+X6yYN479vn8tC146h152vPL+OSX33IW6t30Rlmbegsyg9V89A7mxq9101h2UHeXL2L6ycNIjlRR+sjQUEi7V5cbAwzx/Vn3jfP4aFrx3Gkpo4v/z6XzzyykMVb9ka6PGkFry0r5MF3NjL9F+/zzTmfkLfnn4Hy7MJtALonewQpvqXDiI0xZo7rz6Vj+/Lqsh08+M5Grp29mPNPyuDui0cxqk9KpEuUE5STX0ZmSiKfOb0/v1uYz5+WF5HSJY4BPZPYWlLJJWP70k+n/EaMgkQ6nLjYGK6eOJDLx/Xjd4u28Zv38rj4oQ/53PgB3Dl9pH7htEPL8suYmJXGvRePZtbZQ3l9eRHb9layvfQg8bHGHecPj3SJnZqCRDqsLvGxzDpnGFdnD+SR+Zt55qNt/HlFEbdMzeKr5w7XhY3tRNG+QxSVH+b2wYHbNvdKTuQLZw2JcFVSn8ZIpMNLTUrgPy8ZzbvfPpdLxvZl9gdbOPun7/Ho/M0crtYEkdEuNz9wI7TswWkRrkSaoiCRTmNgWhIPXjOON75+NhMG9+Qnb63ngp/P5/XlO3SGVxTLzS+ja3wso/p2j3Qp0gQFiXQ6o/um8PStk3jh9in07JbAN+Ys54pHFvKJbgEclXLzyxg3MJX4WP26ilb6ZqTTOmNYL/58x1n87MpTKdp3iCseWcjdL69kb8WRSJcmQQerali7cz8TguMjEp0UJNKpxcQYV2UP5L27zuNL5wzllWWFXPB/7/OHJfm6Qj4KLN++j9o6V5BEOQWJCJCcGMe9l4zmzW+czZi+Kdz32mqufmyRZhmOsGXBgfbxgxQk0UxBIlLPiMzuPH/7ZH5+1WnkFVdwya8+5MG3N1JVUxfp0jql3PwyRvRO1qnaUS6sQWJmM8xsg5nlmdk9jbyfaGYvBt9fYmZZweXxZvasma0ys3Vmdm+9dbYFly83s5xw1i+dk5lx5YQBvHvnuVw6ti8PvbuJmQ9/xJqi8kiX1qnU1Tm5+WVkZ2lvJNqFLUjMLBZ4GLgYGANcZ2ZjGjS7DShz9+HAg8BPgsuvAhLdfSwwAfjS0ZAJOt/dx7l7drjqF+mVnMgvrz2dx2/KpqTiCDN/8xEPvr2R6lrtnbSFzcUV7D9co8Na7UA490gmAXnuvsXdq4A5wMwGbWYCzwafvwxMs8AdihzoZmZxQFegCtgfxlpFmjR9TCZvf+scLjutHw+9u4mrfruIbSWVkS6rw1u6LTA+ooH26BfOIOkPbK/3ujC4rNE27l4DlAO9CIRKJbATKAB+7u6lwXUc+JuZ5ZrZrKY+3MxmmVmOmeUUFxe3Rn+kE0tNSuDBa8bxm+tPZ0tw7OSlnO26kDGMFm4uITMlkSHp3SJdijQjnEHS2G3KGv6ra6rNJKAW6AcMAb5tZkOD70919/EEDpl9zczOaezD3X22u2e7e3ZGRsYJdUCkoU+f2o+3vnkOpw1I5bsvr+SbLy6n4khNpMvqcOrqnEWb93LmsHTdRrkdCGeQFAL1b1c2AChqqk3wMFYPoBS4HnjL3avdfQ/wEZAN4O5FwZ97gNcIhI5Im+mX2pXff3Eyd104kj+vKOLTv/qQ1Ts0EN+aNuw+wN7KKs4c1ivSpUgIwhkkS4ERZjbEzBKAa4G5DdrMBW4OPr8SeM8DxwoKgAssoBswBVhvZt3MrDtAcPmFwOow9kGkUbExxh0XjGDOrDM4XF3HZx9ZyJyPCyJdVoexcHPghmRnDk+PcCUSirAFSXDM4w5gHrAOeMnd15jZA2Z2ebDZk0AvM8sD7gSOniL8MJBMICSWAk+7+0ogE1hgZiuAj4G/uvtb4eqDSHMmDUnjjW+czeShadzz6iq++/IKzSjcChbmlZDVK4n+undMu2CdYbAwOzvbc3J0yYmET22d88t3NvLr9/I4uV8Ks2/K1i/BE1RTW8e4B97m8nH9+NEVYyNdTqdmZrmhXGahK9tFWkFsjPHtC0/iyZuzKdh7kMt/vYCPt5Y2v6L8mxWF5VQcqWHqMB2WwwfDAAAPC0lEQVTWai8UJCKtaNroTF772lR6dI3n+scX84cl+ZEuqd1ZmFcCBGZnlvZBQSLSyob3Tua1r01l6vB07nttNT/8y1rNJHwcFm7ey+i+KaR1S4h0KRIiBYlIGPToGs9Tt0zkljOzeGLBVr72/DINwofgcHUtuQVlTNXeSLuiIBEJk9gY4/7LxvC9S0fz1ppdXP/4YsoqqyJdVlTL2VZGVU0dU3Xab7uiIBEJIzPji2cP5dEbxrO6aD/Xzl7MngOHI11W1Hpv/R7iY42JQ9IiXYocBwWJSBuYcUpfnrllItvLDnLNY4vZse9QpEuKOjW1dfx5ZRHnn9Sb5MS4SJcjx0FBItJGzhyeznO3TaKk4ghX/3YRBXsPRrqkqLJw816KDxzhitMbzu0q0U5BItKGJgxO44Xbp1BZVcN1jy+msExhctSfPtlB9y5xnD+qd6RLkeOkIBFpY6f078Hvb5vMgcPVXPf4Yop0mIuDVTXMW7OLS8f2pUt8bKTLkeOkIBGJgFP69+C52yazr7Ka6x9fzK7yzj0A//ba3VRW1fIZHdZqlxQkIhFy2sBUnvnCJEoqqvjcowvZUlwR6ZIi5k+f7KBfjy5MytLZWu2RgkQkgiYM7skLt0/hcHUtV/52ESsL90W6pDZXUnGEDzaVMPP0/sTE6CZW7ZGCRCTCxg7owctfOZOkhFium72YDzd1rltDz11eRG2d62ytdkxBIhIFhqR345WvnMnAtCRufXopry4rjHRJbeJwdS2PfbCZCYN7MjKze6TLkROkIBGJEpkpXXjpy2cwMSuNO19awSPz8+jo9wv63aJt7N5/hO9cdFKkS5EWUJCIRJGULvE884WJXH5aP3761gbun7uG2g46c/D+w9U8Mn8z54zMYMpQTdLYnmkeApEokxgXyy+vGUefHl2Y/cEWSiqO8Iurx3W46yue+GAL+w5W813tjbR7ChKRKBQTY/znJaPp3T2RH/51HXsrPubxm7NJ6RIf6dJaRUnFEZ5YsJVLx/bllP49Il2OtJAObYlEsS+ePZSHrh3HsoIyrnlsMSUVRyJdUqt46J1NHKmp484LR0a6FGkFYQ0SM5thZhvMLM/M7mnk/UQzezH4/hIzywoujzezZ81slZmtM7N7Q92mSEczc1x/nrh5IltLKrj6t4va/czBywrK+P2SfG6cMphhGcmRLkdaQdiCxMxigYeBi4ExwHVmNqZBs9uAMncfDjwI/CS4/Cog0d3HAhOAL5lZVojbFOlwzh2Zwe9vm0xxxRGuasdXwVfX1nHvK6vok9KFuzQ20mGEc49kEpDn7lvcvQqYA8xs0GYm8Gzw+cvANDMzwIFuZhYHdAWqgP0hblOkQ8rOSmPOrCkcqanj6scWs2HXgUiXdNxmf7CFDbsP8MDMU3TPkQ4knEHSH9he73VhcFmjbdy9BigHehEIlUpgJ1AA/NzdS0PcpkiHdXK/Hrz4pTOIMbh29iJW7yiPdEkh21pSyUPvbuKSsX2YPiYz0uVIKwpnkDQ2aU7DE+KbajMJqAX6AUOAb5vZ0BC3Gdiw2SwzyzGznOLizjXlhHRsw3sn89KXzqBrfCzXP76Y5dujf36u6to67vrjChLjYvjvy06OdDnSysIZJIXAwHqvBwBFTbUJHsbqAZQC1wNvuXu1u+8BPgKyQ9wmAO4+292z3T07IyOjFbojEj2y0rvx4pfOIDUpgRufWBL1YfLTt9aTm1/GDz9zCr1TukS6HGll4QySpcAIMxtiZgnAtcDcBm3mAjcHn18JvOeBOSEKgAssoBswBVgf4jZFOoWBaUnMmTWFnt0SuPHJJayI0jB5a/VOHv9wKzedMZiZ43QkuiMKW5AExzzuAOYB64CX3H2NmT1gZpcHmz0J9DKzPOBO4OjpvA8DycBqAuHxtLuvbGqb4eqDSLTrl9qVF2ZNITUpns9HYZhsLankO39cyWkDU7nv0tGRLkfCxDr6pHAA2dnZnpOTE+kyRMJmx75DXDt7EQcO1/DaV6cyJL1bpEui/FA1V/12IXsOHOEv/3EWA3omRbokOU5mluvu2c2105XtIh1A/9Su/OG2KcSYcevTH1NaWRXReqpq6vjyc7lsKa7k4evHK0Q6OAWJSAcxqFcSj9+UTVH5YW7/XQ6Hq2sjUoe7c/crK1m0ZS8/vfJUpg5Pj0gd0nYUJCIdyITBPfnlNePIzS/jzpeWU1Nb16aff6Smlgf+spbXPtnBdy46ic+OH9Cmny+RoSAR6WAuGduX7106mjdW7eIbc5ZT3UZhsjCvhIsf+pCnP9rGLWdm8dXzhrXJ50rkaY4CkQ7oi2cPxR3+5411VNfW8Zvrx5MQF57/N64qLOe372/mr6t2Migtiadvncj5J/UOy2dJdFKQiHRQt58zlPhY47//vJYbnljMp0/tx/hBPRnVtzvxsS0LlfKD1XyYV8zvFuXz8dZSuiXE8vVpI/jqecM63A24pHkKEpEO7JapQ0hKiOMXb2/k/rmBS666xMcwum8KY/v34JR+PRiemcyw9GR6JP37TbNqausoraxia0klW0oq2bj7AEu3lbKmaD/ugbPFvnfpaK6eOLDD3HRLjp+uIxHpJIr2HSI3v4xPCvaxekc5a4rKqaz655ldqUnxJMXHEh8XQ4wZ+w5Wse9QNfV/RSTGxTBuYCpnDkvnjGG9GD8olbgW7t1I9Ar1OhLtkYh0Ev1Su9IvtSuXndYPgLo6J7/0IJv3VLC5uIKC0oMcqamjuraO2jqnR9d4eiUnkp6cwKC0JIZlJNM/tSsxMY3NnSqdmYJEpJOKiTGGpHdjSHo3PoWmdZcTp31SERFpEQWJiIi0iIJERERaREEiIiItoiAREZEWUZCIiEiLKEhERKRFFCQiItIinWKKFDMrBvIjXcdxSgdKIl1EK+go/QD1JVp1lL5EYz8Gu3tGc406RZC0R2aWE8ocN9Guo/QD1Jdo1VH60p77oUNbIiLSIgoSERFpEQVJ9Jod6QJaSUfpB6gv0aqj9KXd9kNjJCIi0iLaIxERkRZRkEQZM7vLzNzM0pt4/2Yz2xR83NzW9TXHzH5gZivNbLmZ/c3M+jXRrjbYZrmZzW3rOkNxHH2J6u8EwMx+Zmbrg/15zcxSm2i3zcxWBfsclbcVPY6+zDCzDWaWZ2b3tHWdzTGzq8xsjZnVmVmTZ2u1i+9Eh7aih5kNBJ4ARgET3L2kwftpQA6QDTiQG2xX1ta1NsXMUtx9f/D514Ex7v7lRtpVuHtymxd4HELpS3v4TgDM7ELgPXevMbOfALj73Y202wZkN/y7F01C6YuZxQIbgelAIbAUuM7d17Z1vU0xs9FAHfAYcJe7NxoS7eE70R5JdHkQ+C6BX0iNuQh4291Lg7+o3gZmtFVxoTj6izeoG033JeqF2Jeo/04A3P1v7l4TfLkYGBDJeloixL5MAvLcfYu7VwFzgJltVWMo3H2du2+IdB2tQUESJczscmCHu684RrP+wPZ6rwuDy6KKmf2PmW0HbgC+30SzLmaWY2aLzewzbVjecQmhL+3iO2ngC8CbTbznwN/MLNfMZrVhTSeqqb60x++lKVH/neie7W3IzN4B+jTy1n3AfwIXNreJRpa1+f/4j9UPd3/d3e8D7jOze4E7gPsbaTvI3YvMbCjwnpmtcvfNYSy7Ua3Ql6j4TqD5vgTb3AfUAH9oYjNTg99Lb+BtM1vv7h+Ep+KmtUJfouJ7CaUfIYiK7+RYFCRtyN0/1dhyMxsLDAFWmBkEdtWXmdkkd99Vr2khcF691wOA+WEp9hia6kcjngf+SiNB4u5FwZ9bzGw+cDrQ5kHSCn2Jiu8Emu9L8ESATwPTvInB0Xrfyx4ze43AIaI2/6XVCn0pBAbWez0AKGq9CkNzHH+/jrWNqPhOjkWHtqKAu69y997unuXuWQT+EYxvECIA84ALzaynmfUksAczr43LPSYzG1Hv5eXA+kba9DSzxODzdGAqEDWDoEeF0hfawXcCgTOYgLuBy939YBNtuplZ96PPCfRlddtVGZpQ+kJgcH2EmQ0xswTgWiAqzw48lvbyneDuekTZA9gGpAefZwNP1HvvC0Be8HFrpGttpPZXCPxFXwn8GejfsB/AmcAqYEXw522RrvtE+9IevpNgjXkExgyWBx+/DS7vB7wRfD40+J2sANYQOPwS8dpPpC/B15cQOHNrczT2BbiCwH8ajwC7gXnt9TvR6b8iItIiOrQlIiItoiAREZEWUZCIiEiLKEhERKRFFCQiItIiChKRYzCzihau/3Lw6v1jtZl/rNlfQ23ToH2Gmb0VanuRllCQiISJmZ0MxLr7lrb+bHcvBnaa2dS2/mzpfBQkIiGwgJ+Z2ergvSGuCS6PMbNHgveV+IuZvWFmVwZXuwF4vd42Hg1OVLnGzP5fE59TYWb/Z2bLzOxdM8uo9/ZVZvaxmW00s7OD7bPM7MNg+2Vmdma99n8K1iASVgoSkdB8FhgHnAZ8CviZmfUNLs8CxgJfBM6ot85UAvcnOeo+d88GTgXONbNTG/mcbsAydx8PvM+/zu0V5+6TgG/WW74HmB5sfw3wq3rtc4Czj7+rIsdHkzaKhOYs4AV3rwV2m9n7wMTg8j+6ex2wy8z+Xm+dvkBxvddXB6cBjwu+N4bA9Cv11QEvBp//Hni13ntHn+cSCC+AeOA3ZjYOqAVG1mu/h8B0GyJhpSARCU1j05IfaznAIaALgJkNAe4CJrp7mZk9c/S9ZtSfw+hI8Gct//y3+y0C8zSdRuAIw+F67bsEaxAJKx3aEgnNB8A1ZhYbHLc4B/gYWAB8LjhWksm/Tim/DhgefJ4CVALlwXYXN/E5McDRMZbrg9s/lh7AzuAe0Y1AbL33RhKNM8VKh6M9EpHQvEZg/GMFgb2E77r7LjN7BZhG4Bf2RmAJUB5c568EguUdd19hZp8QmMF1C/BRE59TCZxsZrnB7VzTTF2PAK+Y2VXA34PrH3V+sAaRsNLsvyItZGbJ7l5hZr0I7KVMDYZMVwK/3KcGx1ZC2VaFuye3Ul0fADM9cC95kbDRHolIy/3FzFKBBOAHHrwhmbsfMrP7CdwrvKAtCwoefvuFQkTagvZIRESkRTTYLiIiLaIgERGRFlGQiIhIiyhIRESkRRQkIiLSIgoSERFpkf8P1+rqsUZYvhQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23f584f4f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alpha is: 0.0038423396552810674\n"
     ]
    }
   ],
   "source": [
    "#可视化\n",
    "mses = np.mean(lasso.mse_path_, axis = 1)\n",
    "plt.plot(np.log10(lasso.alphas_), mses) \n",
    "\n",
    "plt.xlabel('log(alpha)')\n",
    "plt.ylabel('mse')\n",
    "plt.show()    \n",
    "            \n",
    "print ('alpha is:', lasso.alpha_)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
